¿Sergio Consoli is a Scientific Project Officer at the European Commission, Joint Research Centre, Italy, working on the project "Big Data and Forecasting of Economic Developments" aiming at exploring novel big data sources and methodologies to provide better economic forecasting. Formerly Sergio was a Senior Scientist within the Data Science department at Philips Research, a Computer Engineering Officer at the Italian Presidency of the Council of Ministers, and a Junior Researcher at the National Research Council of Italy. Sergio's education and scientific experience fall in the areas of data science, operations research, artificial intelligence, knowledge engineering, and machine learning. He is author of several research publications in peer-reviewed international journals, granted patents, edited books, and leading conferences in these fields. Diego Reforgiato Recupero is an Associate Professor at the Department of Mathematics and Computer Science of the University of Cagliari, Italy, where he is also a member of the Technical Commission for Patents and Spin-offs. His interests span from Semantic Web, graph theory, and smart grid optimization to sentiment analysis, data mining, big data, natural language processing, and human-robot interaction. He is the author of several research publications in peer-reviewed international journals, edited books, and leading conferences in these fields. He is Director of the Laboratory of Human Robot Interaction and Co-Director of the Laboratory of Artificial Intelligence and Big Data. He is also affiliated with the National Research Council of Italy (CNR) where he is a member of the Semantic Technology Laboratory and passionate about bringing the research output to the market.
Michaela Saisana is Head of the Monitoring, Indicators and Impact Evaluation Unit and she also leads the European Commission's Competence Centre on CompositeIndicators and Scoreboards (COIN) at the Joint Research Centre in Italy. She has been working in the JRC since 1998, where she obtained a prize as "Best Young Scientist of the Year" in 2004 and together with her team the "JRC Policy Impact Award" for the Social Scoreboard of the European Pillar of Social Rights in 2018. Specializing on process optimization and spatial statistics, she is actively involved in promoting a sound development and responsible use of performance monitoring tools which feed into EU policy formulation and legislation in a wide range of fields.

Covers the use of data science technologies, including advanced machine learning, Semantic Web technologies, social media analysis, and time series forecasting for applications in economics and finance

Shows successful applications of advanced data science solutions to extract knowledge from data in order to improve economic forecasting models

Primarily targets data scientists and business analysts exploiting data science technologies, and research students in disciplines and courses related to economics and finance

Data Science Technologies in Economics and Finance: A Gentle Walk-In.- Supervised Learning for the Prediction of Firm Dynamics.- Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting.- Machine Learning for Financial Stability.- Sharpening the Accuracy of Credit Scoring Models with Machine Learning Algorithms.- Classifying Counterparty Sector in EMIR Data.- Massive Data Analytics for Macroeconomic Nowcasting.- New Data Sources for Central Banks.- Sentiment Analysis of Financial News: Mechanics and Statistics.- Semi-supervised Text Mining for Monitoring the News About the ESG Performance of Companies.- Extraction and Representation of Financial Entities from Text.- Quantifying News Narratives to Predict Movements in Market Risk.- Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?.- Network Analysis for Economics and Finance: An application to Firm Ownership.